Biofouling Mitigation in Forward Osmosis Using Graphene Oxide Functionalized Thin-Film Composite Membranes
Bibliographic record
Abstract
Forward osmosis (FO) is an emerging membrane process with potential applications in the treatment of highly fouling feedwaters. However, biofouling, the adhesion of microorganisms to the membrane and the subsequent formation of biofilms, remains a major limitation since antifouling membrane modifications offer limited protection against biofouling. In this study, we evaluated the use of graphene oxide (GO) for biofouling mitigation in FO. GO functionalization of thin-film composite membranes (GO-TFC) increased the surface hydrophilicity and imparted antimicrobial activity to the membrane without altering its transport properties. After 1 h of contact time, deposition and viability of Pseudomonas aeruginosa cells on GO-TFC were reduced by 36% and 30%, respectively, compared to pristine membranes. When GO-TFC membranes were tested for treatment of an artificial secondary wastewater supplemented with P. aeruginosa, membrane biofouling was reduced by 50% after 24 h of operation. This biofouling resistance is attributed to the reduced accumulation of microbial biomass on GO-TFC compared to pristine membranes. In addition, confocal microscopy demonstrated that cells deposited on the membrane surface are inactivated, resulting in a layer of dead cells on GO-TFC that limit biofilm formation. These findings highlight the potential of GO to be used for biofouling mitigation in FO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".